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Chemical Imaging and Analysis of Single Nerve Cells by Secondary Ion Mass Spectrometry Imaging and Cellular Electrochemistry

A nerve cell is a unit of neuronal communication in the nervous system and is a heterogeneous molecular structure, which is highly mediated to accommodate cellular functions. Understanding the complex regulatory mechanisms of neural communication at the single cell level requires analytical techniques with high sensitivity, specificity, and spatial resolution. Challenging technologies for chemical imaging and analysis of nerve cells will be described in this review. Secondary ion mass spectrometry (SIMS) allows for non-targeted and targeted molecular imaging of nerve cells and synapses at subcellular resolution. Cellular electrochemistry is well-suited for quantifying the amount of reactive chemicals released from living nerve cells. These techniques will also be discussed regarding multimodal imaging approaches that have recently been shown to be advantageous for the understanding of structural and functional relationships in the nervous system. This review aims to provide an insight into the strengths, limitations, and potentials of these technologies for synaptic and neuronal analyses.

INTRODUCTION
The molecular organization of neurons has been shown to play a regulatory role in neural communication. Investigation of this dynamic nanoscale architecture is crucially important for understanding the mechanistic insight into the brain activity and biology. Besides in vivo studies, neuronal cultures are widely used as models to study neuronal biology, for example, primary neuronal cultures, induced pluripotent stem cell-derived neurons, and neuronal cell lines. The main advantage of neuronal cultures is that their environmental factors, such as temperature, CO 2 and the culture medium, can be controlled and modified in a specific manner. In addition, it is a simplified model with only one or few cell types, allowing to focus on specific cellular mechanisms or particular cellular pathways while eliminating interference from complex interactions regarding the diversity of cells in the brain. To obtain highly spatially resolved images of single synapses, spines, vesicles, etc., imaging technologies with high spatial resolution are needed. To evaluate transmitter release in single synapses, analytical techniques with high temporal resolution are desirable. These criteria can be met using presently available technologies, for instance, fluorescence microscopy and super-resolution microscopy, mass spectrometry imaging (MSI), and cellular electrochemistry. While microscopic techniques have been widely used as established methods in neuroscience and biological research, MSI and cellular electrochemistry are less known in the field (Figure 1). FIGURE 1 | Schematic of the main components of synaptic structure: synaptic vesicles and major associated molecules affecting synaptic activity. Secondary ion mass spectrometry (SIMS) and electrochemistry for measuring synaptic structure and activity, respectively.
Mass spectrometry imaging (MSI) is a powerful imaging tool for obtaining chemical information with cellular and subcellular spatial information. MSI is characterized by high chemical specificity, high sensitivity, high mass resolution, and, often, parallel detection of various compounds. Among different types of MSI, secondary ion mass spectrometry (SIMS) offers highest spatial resolution, which is suitable for subcellular imaging of neuronal cells. Unlike fluorescence imaging, SIMS allows for a label-free approach and elucidation of molecular structure (ToF-SIMS), although isotopic labeling is often employed to track the molecular turnover of cells and organelles (Lanekoff et al., 2011;Steinhauser et al., 2012). SIMS has continuously expanded its horizon of applications to study biomolecular organization in cell biology and neuroscience (Thomen et al., 2020;Agüi-Gonzalez et al., 2021;Jähne et al., 2021).
Electrochemical methods such as amperometry, cytometry, and voltametry measure the current or potential, which is changed by an electrochemical reaction related to an analyte, to quantify its concentration. Catecholaminergic neurotransmitters such as dopamine, epinephrine, and norepinephrine are all electroactive molecules, which can be oxidized or reduced and detected. With the excellent temporal resolution of electrochemical techniques (ns-ms range), single vesicular release events of live cells can be assessed to elucidate the mechanism of release. Electrochemical methods are, therefore, important techniques for studies on neural communication mechanisms and effects of exogeneous elements, e.g., drugs or plasticity inducing stimulation (Li et al., 2016b;Gu et al., 2019).
In this review, we present the principle, technical aspects of SIMS imaging and cellular electrochemistry, and their most relevant applications in the research on synaptic biology. The goal of this review is to introduce these unfamiliar technologies to the neuroscience community, providing an insight of their strengths, limitations, and potentials for synaptic and neuronal imaging and analysis.

SECONDARY ION MASS SPECTROMETRY IMAGING
Secondary ion mass spectrometry utilizes a primary ion beam to bombard the surface of a sample pixel by pixel, sputtering secondary ions, which originate from the sample (Pacholski and Winograd, 1999;Vickerman and Briggs, 2013). Secondary ions will then be extracted and analyzed with a mass spectrometer and eventually be detected based on their mass to charge ratio (m/z). A mass spectrum is obtained for each pixel. Assembly of all spectra corresponding to their coordinates on the surface of the sample results in ion images showing the distribution of detected ions across the sample. During bombardment of the sample surface, the energy of the primary ion beam is partially transferred to the sample surface, the so called "collision cascade" phenomenon is taking place, and molecules in the sample are sputtered and ionized. It should be noted that only a small fraction of sputtered particles is ionized and analyzed. There are a variety of primary ion sources, for example, liquid metal ion guns (LMIGs) Au 3 + , Bi 3 + , polyatomic C 60 + , gas cluster ion beams (GCIBs), and monoatomic Cs + and O − . Detailed development and performance of these primary ion sources can be found in comprehensive bodies of literature (Toyoda et al., 2003;Weibel et al., 2003;Kollmer, 2004;Rabbani et al., 2013). Depending on their properties, they have different performances in terms of spatial resolution, detected ions, destruction of the sample surface, and ionization efficiency. Larger cluster ion beams are softer than the small ones (Postawa et al., 2005;Winograd, 2013 and, thus, are less destructive to the sample and generate less fragmented secondary ions. However, large ion beams have a larger beam size, which hinders lateral resolution. Furthermore, there are several other parameters that also affect secondary ion yield, particularly, ionization probability, sputter yield, and concentration of analytes, and transmission of the instrument.

Time of Flight-Secondary Ion Mass Spectrometry and NanoSIMS
There are two main types of SIMS instruments available in the field. In the following section, distinct instrumental features of these two instruments are discussed. In time of flight (ToF)-SIMS, the primary ion beam impacts the sample at a 45 • angle (Figure 2A), which is one of the factors limiting achieved lateral resolutions. On the other hand, nanoscale SIMS (NanoSIMS) benefits from its coaxial design, and combined with the monoatomic primary ion beam, it provides very high lateral resolution, ∼50 nm for a Cs + source ( Figure 2B). SIMS generally has a depth resolution (z) of 1-10 nm. ToF-SIMS detects various analytes in parallel within a mass range of up to 2,000 Da, whereas NanoSIMS measures elements and small fragmented ions with up to 7 detectors. Mass resolution (M/ M) is ∼10,000 for nanoSIMS and up to 10,000 for ToF-SIMS with delayed extraction (Vanbellingen et al., 2015;Nuñez et al., 2018). In addition, NanoSIMS employs Cs + and O − as primary ion sources, whereas ToF-SIMS has more options from a wide range of primary ion sources mentioned above. Detailed discussion on the configuration of ToF-SIMS and NanoSIMS and their characteristics can be found in selected reviews of literature (Boxer et al., 2009;Nuñez et al., 2018;Agüi-Gonzalez et al., 2019).

Sample Preparation for Secondary Ion Mass Spectrometry Imaging
Sample preparation is a key aspect for SIMS analysis as molecules of interest and their locations need to be preserved well. Depending on the species of interest, different strategies for sample preparation should be considered. The simplest approach in terms of sample handling is the air-drying method in which preserved cells, after multiple washing steps to remove salts etc., are air-dried. This, however, could lead to shrinkage and deformation. Distinct sample preparation protocols could be performed for particular analytes of interest because of complex chemical distribution and topography in cultured neurons (Tucker et al., 2012). Paraformaldehyde fixation retained good lipid signals of Aplysia californica but produced low signals in the cell soma, whereas glycerol preserved well cell mophology and retained good lipid signals in the soma. On the other hand, formaldehyde increased background signal intensity but did not enhance lipid signals. The combination of glycerol and paraformadehyde was suitable for SIMS imaging of cultured neurons. Nevertheless, chemical fixation does not work well for all types of molecules and could potentially cause interferences. To avoid this, frozen hydrated samples have been used. Samples are snap-frozen and maintained in the frozen state during measurement; this is currently the best way to preserves the sample's molecular structure. However, it is less reproducible because of the inherent challenging procedure. One of the challenges is that samples need to be kept at a very low temperature throughout the preparation and away from the atmosphere to prevent the condensation of water on top of the samples. Another challenge is that the instrument needs to be cooled down with liquid nitrogen and maintained at equally low temperature during the entire analysis. This, in turn, causes difficulties in storing and transferring frozen samples into the analysis chamber of the SIMS instrument for analysis. A common alternative, which preserves samples well enough and is easily reproduced, is freeze-drying where samples are snap frozen in isopentane at −180 • C to avoid the formation of larger water crystals. Subsequently, samples are dried in a vacuum at a low temperature so that the water slowly sublimates. Lee et al. (2019) propose covering samples with a graphene layer to let the samples dry slowly to better preserve cellular structures. The graphene layer can then be removed by air plasma treatment, and ion images can be taken with an improved quality compared to air drying. Most recently, Lim et al. (2021) showed that cells can be imaged in the wet state by covering them with grapheme, and that ions are sputtered through a transient hole while cells are still alive. Another possibility of sample preparation FIGURE 2 | Schematic diagram depicting the set-up of (A) ToF-SIMS and (B) nanoscale SIMS (NanoSIMS). In ToF-SIMS, sample surface is bombarded with primary ion beam at an angle of 45 degrees, and secondary ions are sputtered away, extracted, and transferred into the ToF mass analyser, where the ions are separated based on their mass to charge ratio (m/z). For each pixel, a mass spectrum is obtained, and for each mass peak, an ion image is depicted. NanoSIMS can achieve higher lateral resolution owing to the coaxial design of the primary and secondary ion beams. After being sputtered from the sample surface, the secondary ions are separated by a magnetic sector analyzer, and, eventually, seven masses are detected.
is to chemically fix cells with aldehydes and embed them in a resin (epoxy or acrylic resins), which has been commonly used for NanoSIMS. Samples can be contrasted, e.g., with osmium tetroxide to perform correlative transmission electron microscopy. Transparent resins can be used for correlative fluorescence microscopy (Saka et al., 2014).
Analyzing the spatial distribution of biomolecules across a biological sample has been demonstrated to be an important application for SIMS in general. Especially in highly polarized cells such as neurons, gaining the subcellular spatial distribution of cellular molecules is an important step in understanding their biology. SIMS is able to provide extensive atomic and molecular information at subcellular resolution, thus enabling the analysis of neurons, synapses, and vesicles.

Vitamin E in Single Neurons
Many authors have studied the localization of vitamin E, an important antioxidative molecule, in neurons. The molecule is of great interest in neuroscience as low level of vitamin E affects axonal transport and can lead to axonal dystrophy and swelling. Monroe et al. (2005) identified vitamin E peaks with a vitamin E standard at m/z: 430, 205, and 165. Similar peaks were found in the Aplysia neuron with a distinct distribution; particularly, vitamin E localized in the soma. To minimize the effects of sample topography, the signal intensity of vitamin E was normalized to that of an ubiquitous evenly distributed ion, e.g., lipid acyl chain fragment (m/z 69). In addition, the authors highlight the importance of consistent sample preparation/analysis time as this distinct localization could not be observed when the samples were stored for a few days after freeze-drying before analysis (Monroe et al., 2005). The identification of vitamin E peaks was confirmed in Aplysia neurons by Passarelli and Ewing (2013) by tandem MS analysis. The localization of vitamin E was found to be in the soma, co-localizing with carotenoids, which were seen in an optical image. More recently, Bruinen et al. (2018) have imaged α-tocopherol, a type of vitamin E, in neurons ( Figure 3A). Human-induced pluripotent stem cell (iPSC)-derived neurons were imaged in depth, and tandem MS analysis was performed for m/z 430. The data of MS/MS total ion current for m/z 430 as well as its product ions showed the localization of α-tocopherol predominantly in the soma but also in neurites (Bruinen et al., 2018). In addition, it was noted that peak assignments of molecules could be improved by the MS/MS information of other molecules. In this case, m/z 772 was more confidently assigned to phosphatidylcholine (PC) (32:0) [M + K] + and m/z 551 as a fragment of PC (32.0) and DG (32:0). PC (32:0) [M + K] + localized evenly distributed in the soma and in central axes of neurites. Interestingly, m/z 551 showed a different distribution in the outer perimeter of the soma and neurites, which is likely to be attributed to DG (32:0).

Organization and Turnover of Membrane Lipids in PC12 Cells
PC12 rat pheochromocytoma cells are a common, well-studied cellular model system for the investigation of exocytosis mechanisms. They are easy to maintain in a culture. PC12 cells contain large dense core vesicles (LDCVs) with catecholamines (DeLellis et al., 1973). These vesicles can release their content upon extracellular stimulation in a conserved mechanism involving multiple proteins such as SNARE proteins. This has led to the investigation of PC12 cells with different SIMS techniques. Main emphases of the studies were lipid analysis mainly with ToF-SIMS and analysis of LDCV with NanoSIMS. In the study by Lanekoff et al. (2011), PC12 cells were incubated with isotopic phospholipids of two different shapes. Incubation with cylindrical-shaped lipid PC and conical-shaped phosphatidylethanolamine (PE) was shown to alter the exocytosis rate in opposing ways (Uchiyama et al., 2007); therefore, ToF-SIMS was conducted to assess the incorporation of these lipids. The cells were incubated with lipids containing rare isotopes [deuterated 16:0/16:0 PC (D75PC) and deuterated 16:0/16:0 PE (D62PE)], which incorporated into newly synthesized lipids in cells and could be distinguished from endogenous molecules. Lanekoff et al. showed that small changes in lipids (0.5% for PC, 1.3% for PE) were observed in the plasma membrane. With increasing concentrations of up to 300 µM during incubation, the relative amount of PC and PE in the membrane also increased to ∼ 2% for PC and ∼ 9% for PE. In 2018, relative quantification of isotopic fatty acids in PC12 cells as well as their lipid turnover, a process in which old lipid molecules are gradually replaced by newly synthesized ones to maintain proper cellular functions, were investigated by Philipsen et al. (2018). A-linolenic (omega 3 fatty acid) and linoleic acids (omega 6 fatty acid) are the precursors for polyunsaturated fatty acids and lipids and have been implicated in neurological disorders (Parletta et al., 2016). Incubation of PC12 cells with α-linolenic acid and linoleic acid leads to their uptake and incorporation into PCs, PEs, and phosphatidylinositols (PIs) ( Figure 3B). Interestingly, it was shown that the formation of polyunsaturated fatty acids is different depending on which isotopically labeled fatty acid was incubated. Linoleic acid was only converted to 20 carbon polyunsaturated lipids, whereas α-linolenic acid could be converted to 20 carbon and 22 carbon polyunsaturated fatty acids. Furthermore, linoleic acid and its conversion product, arachidonic acid, were found to make up lipids with two and four double bonds, while αlinolenic acid and its product, eicosapentaenoic acid, make up lipids with three, five, and six double bonds, meaning that no addition of double bonds takes place during incorporation (Philipsen et al., 2018).

Structural Effects of Membrane Lipids on Synaptic Plasticity
Synaptic plasticity associated with lipid alterations has recently been investigated by Gu et al. (2020). PC12 cells were stimulated 0, 3, or 6 times with a 100-mM K + solution, and lipid fragments were analyzed with an ION-TOF V instrument. The authors focused on the fragments from PCs, PEs, and PIs, as they appear in higher abundance in the plasma membrane than other lipids. Interestingly, PC level reduced with increasing stimuli, whereas PE, PI, and cholesterol levels increased. It was hypothesized that this alteration might stabilize the fusion pore of vesicles during exocytosis owing to high abundance of high curvature, such as conical (PE), inverted cone shape (PI) lipids, and intrinsically negatively curved cholesterol. This, in turn, would lead to higher fraction of vesicular content to be released after multiple stimuli (Gu et al., 2020). The data agree with the results from previous studies on patients with Alzheimer's disease (Prasad et al., 1998), cocaine-treated flies , and cognitiveenhanced methylphenidate-treated flies , suggesting a link among dynamic lipid organization, synaptic plasticity, and memory.
Agüi-Gonzalez et al. (2021) investigated the effect of neuronal activity alterations on the lipid organization of the plasma membrane in primary hippocampal neurons. First, the spatial organization of lipids and lipid fragments across different parts of the cellular membrane was assessed. Independent component analysis and neighborhood cross correlation coefficient analysis was conducted to differentiate differences in lipid localization between neurites and the cell body. Second, neurons were treated with tetrodotoxin, a voltage-gated sodium channel blocker, to inhibit neuronal activity, or with bicuculine, a competitive antagonist of GABA A receptors, to increase neuronal activity. 51 lipid related peaks were found significantly different in their relative abundance between the activity inhibited cells and the control, and 41 lipid related peaks changed compared between the activity stimulated cells and the control. For example, ceramides, which are second messengers and components in the sphingolipid metabolic pathway, increased their abundance in inhibited cells. In addition, the total PC level decreased in the cell body while it increased in neurites in both activityinhibited and enhanced cells. Furthermore, phosphatidylserine abundance reduced in low-activity cells, whereas it increased in high-activity ones. These distinct lipid alterations provide new insights into possible mechanisms of activity-dependent molecular organization in neurons.

CORRELATIVE SECONDARY ION MASS SPECTROMETRY AND OTHER TECHNIQUES FOR IMAGING SINGLE VESICLES AND SYNAPSES
Recently, emerging correlative imaging has expanded the perspectives of many complex biological questions that need multidimensional information, which cannot be achieved with a single technique. Correlative imaging combines several different imaging and analysis technologies to connect different properties of cells, e.g., cellular structures, functions, and molecular dynamics to obtain an insight into their molecular mechanistic process. Various combinations such as SIMS, electron microscopy, and fluorescence microscopy have been optimized (Watanabe et al., 2011;Wilhelm et al., 2014;Salamifar and Lai, 2015;Lange et al., 2021). Here, we describe several correlative approaches, which have been applied for imaging single vesicles and synapses.

Correlative NanoSIMS and Fluorescence Microscopy
Correlative NanoSIMS and super resolution-stimulated emission depletion (STED) microscopic imaging was developed to study protein turnover in specific subcellular structures of rat hippocampal neurons (Saka et al., 2014). Neurons were incubated with 15 N-leucine, which was then incorporated into newly synthesized proteins as a marker for protein turnover in neurons. Several organelles and structures, particularly active zones, vesicles, mitochondria, Golgi, and endoplasmic reticulum, were immunostained and visualized by STED microscopy. Correlation between the map of protein turnover from NanoSIMS and protein localization from STED microscopy showed that the protein turnover in the synapse is significantly higher than that in axonal regions.
The combination of isotopic 15 N-leucine labeling and immunostaining for specific vesicular proteins in rat hippocampal neurons was used to study the protein turnover of recycling vesicles by NanoSIMS and STED microscopy (Truckenbrodt et al., 2018). By labeling recycling vesicles with an antibody against the intravesicular domain of synaptotagmins in different endocytosis cycles, the protein turnover of actively recycling vesicles could be compared to that of older vesicles. It was found that actively recycling vesicles are more enriched at the isotopic label and, therefore, have more newly produced proteins.
Correlative NanoSIMS and STED imaging was also performed to investigate protein turnover in single synapses with respect to synaptic activity in rat hippocampal neurons (Jähne et al., 2021). It was found that protein turnover is well-correlated with synaptic activity at the single synapse level. Combined with mathematical modeling of the synaptic vesicle cycle, the authors confirmed this observation by identifying the localization of synapses by STED microscopy and assessing their protein turnover by NanoSIMS using 15 N-labeled leucine incubation technique. Pre-and post-synapses were marked with synaptophysin1 and Homer1, respectively. The intensity of both markers was not correlated with protein turnover; however, when examining only vesicles undergoing exo-and endocytosis, their synaptotagmin intensity correlated strongly with presynaptic protein turnover (Figures 4A-C). Interestingly, this correlation was abolished when the synaptic activity was altered by tetrodotoxin and bicuculine. Both drugs had an opposite effect on both pre-and postsynaptic protein turnover, which indicates that synaptic protein turnover closely depends on synaptic activity (Jähne et al., 2021).

Correlative NanoSIMS and Electron Microscopy
The content of LDCVs has been evaluated by NanoSIMS imaging (Lovrić et al., 2017;Thomen et al., 2020;Nguyen et al., 2022;  Rabasco et al., 2022). Lovrić et al. investigated the distribution of dopamine across LDCVs by incubating PC12 cells with 13 C-labeled L-DOPA, which is then converted to dopamine and loaded into vesicles. Correlating transmission electron microscopy (TEM) and NanoSIMS, LDCVs could be identified, and their substructures, the dense core and halo, could be distinguished. The distribution of 13 C-labeled dopamine across these substructures could also be analyzed. In addition, reserpine, an inhibitor of the vesicular monoamine transporter (VMAT) for inhibiting the loading of dopamine into vesicles, was used in this study to modulate vesicular content. Interestingly, the results indicated that the protein-rich dense core (containing, e.g., chromogranins) traps more dopamine compared to an "expandable labile pool" of dopamine in the halo with increasing incubation time with 13 C-L-DOPA. NanoSIMS results were additionally confirmed by electrochemical data (Lovrić et al., 2017). Thomen et al. (2020) performed an absolute quantification of 13 C-labeled dopamine level in vesicles of PC12 cells. Importantly, the concentration of carbon in the embedding material, epoxy resin, measured via the 12 C 12 C − signal, was similar to that in PC12 cells including vesicles. In addition, to reach a steady state of sputtering, primary ion fluence must reach 1 × 10 17 Cs + cm −2 , which consumed a sample depth of ∼180 nm. Accumulation of dopamine in the dense core of vesicles was observed to be similar to the results from Lovrić et al. (2017). The determined concentration of 13 C-dopamine in vesicles was ∼60 mM, which is in agreement with the electrochemical data (Thomen et al., 2020). More recently, the absolute concentration of dopamine in each of two vesicle compartments was quantified ( Figure 4D; Rabasco et al., 2022). It was found that the dense core contains a dopamine concentration of 87.5 mM, which is significantly higher compared to that in the halo (16 mM). Another study confirmed partial exocytotic release in PC12 cells, which is visualized by correlative NanoSIMS and TEM . 13 C-labeled L-DOPA was incubated in cells for visualization of 13 C-dopamine in vesicles. In addition, 127 I-di-N-desethylamiodarone was introduced to cell media during KCl stimulation of cells. Exocytosed vesicles were then identified by the co-localization of 127 I-di-N-desethylamiodarone and 13 Clabeled dopamine signals inside the vesicles in NanoSIMS images.
This allowed for the direct comparison of dopamine content of vesicles between stimulated and non-stimulated cells. The result showed that the dopamine content of vesicles reduces significantly after they undergo exocytosis, which confirmed the partial release of exocytosis.

AMPEROMETRY FOR STUDYING NEUROTRANSMITTER RELEASE DURING EXOCYTOSIS
Amperometry is one of the electrochemical methods that provides excellent sensitivity for quantification of neurotransmitters secreted from single cells at attomole to zeptomole (Chen et al., 1994;Jaffe et al., 1998;Pothos et al., 1998;Hochstetler et al., 2000), and an outstanding temporal resolution at sub-millisecond level (Robinson et al., 2008). In amperometry, a microelectrode is placed adjacent to a cell surface while a cell is stimulated with a potassium solution to initiate exocytosis. The potential is applied to an electrode surface and kept constant whereas a reduction and oxidation current will be recorded for the detection of an electroactive analyte, in this case the neurotransmitters released from the cell. A carbon fiber microelectrode was first introduced by Gonon et al. (1980) to record neurotransmitter release (Ponchon et al., 1979). In the early 1990s, single cell amperometry (SCA) was described by the Wightman group to study individual exocytosis events by placing a disk-shaped electrode on top of single bovine chromaffin cells Wightman et al., 1991). Carbon fibers have prominent mechanical and electrical properties, including high stability, long-term durability, and negligible temperature sensitivity. These electrodes can be constructed by aspirating carbon fibers (usually 5-10 µm in diameter) into glass or silica tubes to have a cylinder or a disk shaped with a 45 • angle electroactive surface. Therefore, any electroactive molecules present on the surface of the electrode will react, causing a change in current (Bard and Faulkner, 2001;Borland and Michael, 2007). The recording of an amperometric peak with the presence of electroactive transmitters is shown in Figure 5A. According to Faraday's law, the number of transmitter molecules released from each exocytosis event can be calculated with N = Q/nF, where Q is the charge in coulombs, n is the number of electrons transferred per mole in the oxidation reaction (2 electrons for the oxidation of catecholamines and serotonin), and F is Faraday's constant (96,485 C mol −1 ). The limitation of this method is that only neurotransmitters in vesicles with accessible redox waves such as dopamine, adrenaline, noradrenaline, serotonin, and histamine can be detected. Another drawback of the technique is the lack of a qualification analysis because the applied constant potential over time cannot distinguish different electroactive analytes measured at the same time (Borland and Michael, 2007). Therefore, it is necessary to identify molecules before amperometric measurements.

Different Modes of Exocytosis
SCA has been employed as a powerful tool to determine different modes of exocytosis. This phenomenon involves a fusion between a secretory vesicle and the plasma membrane, resulting in the release of its chemical content into the extracellular environment. The formation of a fusion pore is facilitated by SNARE proteins, but what exactly happens to the fusion pore afterward is still controversial (Ren et al., 2016). During quantal release, also called full release, a vesicle fully collapses and merges with the plasma membrane, leading to discharge of the entire content of the vesicle. The mechanism of exocytosis was thought to be an all-or-none process for a long time. However, more recent research has proved that this is not the only mode of release. There are a number of variations depending on how long the pore stays open and how much of the vesicular content is released. One of them is the kiss-and-run mechanism, in which an initial fusion pore can be transiently formed, and the vesicle releases a small amount of its content and closes. The third mode of exocytosis is referred to partial release, also known as sub-quantal release, or extended kiss and run, or open and closed. This mode involves the expansion of small sized fusion pores to a certain degree, allowing a larger amount of neurotransmitters to escape in comparison to that in the kiss and run mode. Illustrations of various modes of release are shown in Figure 5B. Flickering is another mode by which a pore opens and closes many times with only a very small fraction of catecholamine released (Staal et al., 2004). In this case, vesicles can open and close their fusion pores multiple times before expelling all the content. This phenomenon seems to appear more frequently than full release (Mellander et al., 2012).

Amperometry for Quantifying Neurotransmitters in Single Cell Models
Owing to outstanding spatial-temporal resolution and high sensitivity, amperometry has become a powerful tool to study exocytosis for many cell types, including primary cultures, nerve cells, other secretory cell models (chromaffin cells, pheochromocytoma cells, PC12 cells, mast cells, and pancreatic β cells), neurons, synapses, and single nerve cell varicosities (Keighron et al., 2020). Quantal release was observed from axonal varicosities of midbrain dopamine neurons consisting of small synaptic vesicles by Pothos et al. (1998). A 5-µm diameter carbon electrode was placed gently onto a varicosity, which was visualized by labeling neurites with Lucifer yellow. These results provided a new mean for direct measurement of the number of neurotransmitter molecules and release duration of quantal events in central nervous system (CNS) neurons. The data also demonstrated that synaptic strength can be modulated by changing the number of dopamine molecules, or quantal size, released per exocytotic event. Zhou and Misler reported the detection of quantal catecholamine release on the surface of somas of cultured neurons by placing a carbon fiber electrode into the cleft between the somas of superior cervical ganglion neurons (Zhou and Misler, 1995). They found that 3.5 × 10 4 catecholamine molecules were released per packet, or per quantum, indicating that release from neurons is almost 80fold smaller than that from adrenal chromaffin cells. Another study on exocytosis events was carried out by Chen and Ewing in the cell body of single dopamine-containing neurons of Planorbis corneus (Chen and Ewing, 1995). Using a CNS stimulant, amphetamine, they manipulated the size and distribution of different classes of vesicles, which were distinguished by distinct distributions of the released dopamine from the neuron after the stimulation. Their results indicated a coexistence of two different classes of vesicles in dopamine containing neurons of Planorbis. Dopamine level was decreased by 40%, with changes in both vesicular content and distributions after amphetamine treatment. Moreover, amphetamine showed different effects on the two classes of vesicles, resulting in a third class of vesicles. The data demonstrated that multiple classes of vesicles are released, and that vesicular content can be independently manipulated with a psychostimulant. In 1995, Bruns and Jahn monitored the amount of transmitter released and kinetics of exocytosis from individual synaptic vesicles (Bruns and Jahn, 1995). The results indicated that the exocytosis of small vesicles appears more frequently and more quickly after a single action potential compared to large dense core vesicles because of different properties of their release events regarding charge, amplitude, and kinetics. Release rates of both types of vesicles are quite fast; large dense core vesicles discharge their content during the first millisecond, while small vesicles start releasing at a sub-millisecond timescale, suggesting rapid opening formation of a preassembled fusion pore. It was shown as a possible hypothesis that small vesicles are recycled without experiencing full fusion process.

Quantifying Neurotransmitters in Single Neuronal Varicosities and Synapses
Drosophila melanogaster, known as fruit fly, is an excellent model for studying brain function and neuronal communication due to its well-defined and easily manipulated nervous system. Despite its relatively simple genomes, the fly brain displays many high-order brain functions comparable to human counterparts. Therefore, Drosophila has been investigated to understand many important mechanisms of human developmental and physiological processes. Fly larva have been successfully developed to study the release in the field of in vivo electrochemistry. Majdi et al. (2015) reported a novel method to examine the octopamine release of exocytosis events in type 2 varicosities from a live, dissected larval system by amperometry. The neuromuscular junction is located peripherally and can be easily accessed with a small carbon fiber microelectrode. Furthermore, octopaminergic varicosities locate in the larval body wall, which allows an electrode to be easily positioned on these boutons from a filet of the muscle wall (Figures 6A-D). The light-activated ion channel, channelrhodopsin-2, in neuronal varicosities (type 2 optopaminergic boutons) was expressed with the m-Cherry fluorescent protein. A 5-µm carbon fiber microelectrode was positioned on top of a bouton while it was visualized under red light, and exocytosis was evoked by the activation of channelrhodopsin-2 with blue light. The number of octopamine molecules released from the varicosity was determined to be ∼22,000 per vesicle. Interestingly, different modes of release were observed based on different shapes of transients, which was considered related to the mechanism of pore opening of a vesicle to form a nanometer-wide fusion pore ( Figure 6E). It was also indicated that a vesicle fusion pore just opens to release the right amount of neurotransmitters at a necessary rate. This partial release of transmitters possibly affects presynaptic plasticity.
Because of the small distance of a synaptic cleft between neurons (20-30 nm) (Cox and Gabbiani, 2010), it has been very difficult to measure the neurotransmitters released in an individual synapse between two communicating cells. Recently, a novel finite conical nanoelectrode with a 50-to 200-nm tip diameter was introduced for measuring individual vesicular exocytosis events at the synaptic level (Li et al., 2014). The nanotip electrode was fabricated by flame-etching a carbon fiber to form a needle-shaped electrode with less than 100-nm radius and 1-µm shaft length. The release of norepinephrine from single synapses was probed by placing the electrode inside the space between neuronal varicosities and the soma of superior cervical ganglion cells (Figure 7). The results showed two types of amperometric peaks with approximately 42% of single events and 58% of complex events. This indicated the coexistence of different types of vesicle fusions in a single synapse. Additionally, a comparison between exocytosis events inside a synapse and on top of the corresponding varicosity denoted a non-uniform distribution of active zones corresponding to these two peak types. The results suggested that neurons can adopt different vesicle fusion modes in different locations of their axonal varicosities. In another study, a quantification of dopamine release inside single dopaminergic synapses with a nanoelectrode was performed for the first time (Tang et al., 2019). Harpagide, a natural product known to be a neuroprotective drug, was unambiguously demonstrated to enhance synaptic dopamine release and restore dopamine release to normal level from damaged neurons of a Parkinson's disease model. Phosphorylation and aggregation of α-synuclein monomers in neurons are suppressed by harpagide by inhibition of intracellular reactive oxygen level. This leads to an increase in exocytosed dopamine by increasing release frequency and total released amount, thus promoting synaptic activity.

Biosensors for Measuring Non-electroactive Neurotransmitters in Single Neurons
Amperometric techniques with carbon fiber micro-or nanoelectrodes have been commonly used to directly quantify electroactive neurotransmitters in individual exocytotic events. However, it is impossible to monitor non-electroactive neurotransmitters such as acetylcholine, glutamate (Glu), and γ-aminobutyric acid (GABA). Shen et al. (2018) investigated acetylcholine concentration and release dynamics with a 15-nm nanopipette electrode without any modifications. The pipette electrode with nanoscale tip was filled with an organic reagent and then placed in a biological environment, recordings are obtained via ionic fluctuations caused by ion transfer between two immiscible electrolyte solution (ITIES) interfaces. With nanoscale scanning electrochemical microscope assistance, a nanoscale ITIES can be accurately positioned at a single living Aplysia synapse to measure the dynamics of cholinergic transmitter release. This study revealed that acetylcholine release from Aplysia varicosity is Ca 2+ -dependent, and that it consists of singlet, doublet and multiplet spikes, which implies the possibility of multiple vesicle release or flickering of the fusion pore.
In another example, an enzymatic biosensor was successfully developed by co-modification of glutamate oxidase (GluOx) and platinum nanoparticles on the surface of a carbon fiber electrode by the Huang group (Qiu et al., 2018). This has been known as the first direct electrochemical detection of glutamate (Glu) released by exocytosis. The sensor was placed at a single hippocampal varicosity with stimulation of high K + concentration during SCA experiments. Multiple well-defined amperometric transients of exocytosis events were obtained in the presence of GluOx on the electrode. Recently, Yang et al. (2021) developed an electrochemical Glu-nanobiosensor to measure in real-time Glu fluxes released via exocytosis from individual living neurons. An enzyme, GluOx, was immobilized onto a carbon-modified SiC nanowire to convert Glu into H 2 O 2, which is an electroactive molecule. The electrode was placed laterally on top of the varicosities of unpaired (without postsynaptic partner) rat hippocampal axons, and exocytosis release was triggered by a high potassium solution, leading to a series of amperometric spikes (46% occurrence frequency of single event, and the remaining had complex shapes). The study suggested a regulatory mechanism occurring during the exocytosis process to control the size, dynamics and lifetime of fusion pores, which, in turn, affects Glu release during synaptic transmission in the hippocampus.

VESICLE IMPACT ELECTROCHEMICAL CYTOMETRY AND INTRACELLULAR VESICLE IMPACT ELECTROCHEMICAL CYTOMETRY FOR MONITORING NEUROTRANSMITTER STORAGE IN INDIVIDUAL VESICLES
Amperometry provides an excellent method for quantification of the release process at the single cell level; however, it is unable to measure total vesicular content. This can be obtained by vesicle impact electrochemical cytometry (VIEC), and intracellular vesicle impact electrochemical cytometry (IVIEC) developed by the Ewing group Li et al., 2016a;   Figures 8A,B). These two methods share several similarities, but setups are different. In VIEC, a 33-µm disk-shaped electrode is directly dipped into a suspension of isolated vesicles. Vesicles are stochastically ruptured, and their transmitters are released to the surface of the electrode upon the application of a potential. With a similar principle, however, IVIEC employs a carbon fiber nanotip electrode (diameter < 100 nm), which allows for the electrode to penetrate into the cytosol of a living cell and measure the content of individual vesicles in situ. Using Faraday's law, the number of molecules stored inside individual vesicles can be determined similarly to SCA.
The combination of SCA and IVIEC can be applied to study transmitter release and storage in secretory vesicles, and helps to unambiguously certify the quantal or sub-quantal release in different modes of exocytosis in single living cells. Fraction release, the ratio of the amount released and total vesicle content, has been shown to be affected by drug treatments (Ye et al., 2018;Zhu et al., 2019). This provides powerful methodologies allowing for the analysis of the physical properties of vesicles in order to obtain a better understanding of vesicle dynamics. Additionally, these techniques enable the understanding of how alteration of the fraction of release, under the effects of an exogenous factor, can modulate synaptic and brain activities.
Using a combination of SCA and IVIEC, Larsson et al. (2020) showed that the release of octopamine during exocytosis in Drosophila larval neuromuscular junction is sub-quantal and complex. In this study, a nanotip electrode was used to pierce through the muscle into octopaminergic varicosities, which were observed under a fluorescence microscope (Figures 8C-E). Total vesicle content was detected in the range of 200,000 to 1.3 million molecules per vesicle, with a median of 411,000. This value is much higher than the amount released (22,700 molecules per vesicle) previously measured by SCA (Majdi et al., 2015). The fraction release of octopamine is, therefore, very small, and accounts for 4.5% for single events and 10.7% for complex events (oscillating or flickering) compared to the total vesicle content. A fusion pore can be adjusted via the opening-and-closing process to increase the released amount in complex spikes compared to simple ones. Their results strongly demonstrated that presynaptic plasticity can be regulated by fluctuations of fusion pores in the partial release.
Recently, intravesicular Glu was quantified using a novel Glu biosensor in living neurons (Yang et al., 2021). A combination of SCA and IVIEC enables the determination of Glu release and intracellular storage from rat hippocampal neurons following L-glutamine and Zn 2+ treatments, which are known to be involved in the learning and memory processes. Intravesicular contents were strongly affected in opposite ways under the two treatments. Glu vesicular content and released amount dramatically increased by 259 and 183%, respectively, under the L-glutamine treatment. However, fraction release was at a similar level compared to the control group undergoing no L-glutamine treatment. In contrast, under the Zn 2+ treatment, Glu content decreased by ca. one half, while the released amount increased by ca. one third of the total content. L-glutamine significantly enhanced the loading and synthesis of intracellular Glu but did

OTHER ELECTROCHEMICAL METHODS
Another common technique is fast scan cyclic voltammetry (FSCV), which is useful for monitoring neurotransmitter fluctuations in tissues or single cells in vitro because of interferences of complex environments. It has been considered as the most common method for monitoring neurotransmitter level in the brain (Robinson et al., 2003(Robinson et al., , 2008Venton and Wightman, 2003;Rodeberg et al., 2017;Roberts and Sombers, 2018). Electroactive species are quickly reduced and oxidized on electrode surface by a triangular waveform applied to the electrode at high scan rate (>100 V/s). Electroactive compounds in the CNS of a rat brain were measured for the first time using implanted graphite paste microelectrodes by the Adam group (Kissinger et al., 1973). The Wightman group successfully introduced FSCV to determine electroactive transmitters such as dopamine, norepinephrine, epinephrine, serotonin, histamine, and adenosine, from various samples, including nucleus accumbens, bovine adrenal medullary cells, mast cells, and brain tissues (Bucher and Wightman, 2015). Venton and coworkers implemented a method for monitoring dopamine release in a ventral nerve cord from Drosophila larva (Vickrey et al., 2009). They also determined extracellular serotonin and dopamine levels in a single Drosophila larva ventral nerve cord (Borue et al., 2009). ChR2 was genetically expressed in octopaminergic and serotonergic neurons, neurotransmitter released was detected by FSCV with blue light stimulation. These studies show that dopamine and serotonin regulations are analogous to those in mammals. Chronoamperometry and differential pulse voltammetry are available for measuring concentrations of neurotransmitters and different analytes. However, these methods show limited chemical selectivity and poor time resolution, as one scan takes more than 10 s for chronoamperometry and more than 30 s for differential pulse voltammetry. The fluctuation of neurotransmitters is on the sub-second time scale; therefore, these techniques are not commonly employed for monitoring chemical communication in the nervous system.
Capacitance measurement is also a useful approach for monitoring changes of cellular surface area after the vesicular membrane has fused with the plasma membrane during exocytosis (Lindau and Neher, 1988;Gillis, 1995). It allows for direct characterization of fusion pore properties. Capacitance measurements have been conducted for cells and neurons with complex branching (Kushmerick and von Gersdorff, 2003;Kim and Von Gersdorff, 2010). Example study is whole-cell recording from mossy fiber boutons in hippocampal neurons (Hallermann et al., 2003), in axon terminals of the brainstem calyx of Held (Sun and Wu, 2001;Wölfel and Schneggenburger, 2003), and in axon terminals of neurons in the posterior pituitary gland (Hsu and Jackson, 1996). Standard capacitance measurement, however, can only measure readily releasable vesicles and, thus, is not applicable for total releasable pools.

SUMMARY AND FUTURE PERSPECTIVES
Measurement of molecular organization, turnover, and dynamic change in single neuronal cells and single synapses is possible using current state-of-the-art analytical technologies (Table 1). SIMS allows for the visualization of non-targeted and targeted molecules and their metabolic turnover, whereas single cell amperometry and cytometry are ideal quantification methods with high temporal resolution for neuronal secretion. These technologies offer a unique opportunity to obtain an insight into the molecular mechanism that modulates synaptic activity and neuronal processes.
Challenges in neuronal and synaptic imaging are the required very high spatial resolution to visualize extremely small structures, sufficient sensitivity to detect analytes in a very small volume, and a suitable sample preparation strategy to preserve the intact molecular architecture of the sample. Technical developments in these technologies are still continuously ongoing to circumvent these challenges. For example, new primary ion sources and ion optics in SIMS are being developed to improve imaging sensitivity, spatial resolution, and mass resolution. Tandem MS SIMS, such as the recently developed OrbiSIMS (Passarelli et al., 2017), will be useful for elucidating molecular structures in complex molecular compositions of neurons and synapses. In addition, development of labeling tools for large biomolecules, such as proteins (Kabatas et al., 2019;Agüi-Gonzalez et al., 2021), will allow for targeted imaging of multiple proteins and lipids by SIMS at the single synapse level. Furthermore, standardization of data treatment and statistical analysis, as well as development of mass peak databases specific for neurons and synapses will ensure reliable data handling and interpretation performed by those who are not SIMS experts. This will play a vital role in realizing SIMS as a common imaging technique in neuroscience.
The development of new types of micro-and nanoelectrodes for electrochemical analysis is in continuous progress to expand its repertoire of detectable analytes, improve sensitivity and speed, and enable sub-vesicular measurements. Modification of electrochemical conditions, for example adding chaotropic anions (SCN − ) (He and Ewing, 2022), could change the opening nature of vesicles, facilitating the measurement of transmitter content in individual sub-vesicular compartments, the halo, and dense core.
Finally, a combination of different imaging and analysis techniques to obtain multidimensional information of the studied samples will be an increasingly favored trend. We will be able to correlate the organization of a wide range of targeted molecules, neuronal structures and morphology, dynamic metabolic processes, and synaptic activity in single neurons and synapses.


Identification of pyroptosis-related lncRNA signature and AC005253.1 as a pyroptosis-related oncogene in prostate cancer

Background Pyroptosis and prostate cancer (PCa) are closely related. The role of pyroptosis-related long non-coding RNAs (lncRNAs) (PRLs) in PCa remains elusive. This study aimed to explore the relationship between PRL and PCa prognosis. Methods Gene expression and clinical signatures were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A PRL risk prediction model was established by survival random forest analysis and least absolute shrinkage and selection operator regression. Functional enrichment, immune status, immune checkpoints, genetic mutations, and drug susceptibility analyses related to risk scores were performed by the single-sample gene set enrichment analysis, gene set variation analysis, and copy number variation analysis. PRL expression was verified in PCa cells. Cell Counting Kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, transwell, and Western blotting assay were used to detect the proliferation, migration, invasion, and pyroptosis of PCa cells, respectively. Results Prognostic features based on six PRL (AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852) were constructed, and patients in the high-score group had a worse prognosis than those in the low-score group. This feature was determined to be independent by Cox regression analysis, and the area under the curve of the 1-, 3-, and 5-year receiver operating characteristic curves in the testing cohort was 1, 0.93, and 0.92, respectively. Moreover, the external cohort validation confirmed the robustness of the PRL risk prediction model. There was a clear distinction between the immune status of the two groups. The expression of multiple immune checkpoints was also reduced in the high-score group. Gene mutation proportion in the high-score group increased, and the sensitivity to drugs increased significantly. Six PRLs were upregulated in PCa cells. Silencing of AC005253.1 inhibited cell proliferation, migration, and invasion in DU145 and PC-3 cells. Moreover, silencing of AC005253.1 promoted pyroptosis and inflammasome AIM2 expression. Conclusions Overall, we constructed a prognostic model of PCa with six PRLs and identified their expression in PCa cells. The experimental verification showed that AC005253.1 could affect the proliferation, migration, and invasion abilities of PCa cells. Meanwhile, AC005253.1 may play an important role in PCa by affecting pyroptosis through the AIM2 inflammasome. This result requires further research for verification.

Introduction
Prostate cancer (PCa) is the second most common cancer in the male population worldwide and is one of the top five causes of cancer-related death (1). Epidemiological examinations and clinical studies have found that the incidence of PCa is still rising (2). The symptoms of patients with early-stage PCa are non-specific, so PCa is often found in the middle and late stages, and surgical treatment at this time has a poor prognosis and a low survival rate (3). With several important recent discoveries in immune mechanisms and advanced molecular diagnostic platforms, immunotherapy is emerging as a viable option for PCa, especially castration-resistant PCa, to stimulate antitumor immunity (4,5). Different patient responses to the same immunotherapy have been observed in patients with different types and stages of cancer (6). Moreover, the patient response depends on multiple factors, including intratumoral heterogeneity and prior treatment history, suggesting that the need for individualized and combined therapy is an important direction for future successful immunotherapy (7,8). Diagnostic, prognostic, and predictive biomarkers enable patient-specific management of PCa (9). Specific biomarkers to facilitate the clinical selection of immunotherapy patients include programmed death ligand 1 (PD-L1) and prostatic acid phosphatase (PAP), but these approaches are limited by tumor heterogeneity or small percentage populations (10,11). Therefore, there is an urgent need to identify new and effective biomarkers to establish a prognostic model of PCa.
Pyroptosis, distinct from apoptosis, is a type of programmed cell death induced by the inflammasome and carried out by gasdermin proteins (12). It is characterized by cell rupture and many pro-inflammatory factors being discharged (13,14). Pyroptosis affects tumor cell invasion, multiplication, and migration, affecting cancer prognosis (15). The association between pyroptosis and cancer is highly intricate because as a way of cell death, pyroptosis could inhibit cancer occurrence and development. Meanwhile, the release of inflammatory mediators and various signaling pathways in pyroptosis is associated with tumorigenesis and resistance to chemotherapy (16,17). Due to the close association between pyroptosis and cancer progress and prognosis, various prognostic biomarker studies based on pyroptosis genes have been identified and used to construct gene signatures with predictive power. For example, risk signatures based on five pyroptosis-related genes (PRGs) were biomarkers to predict the immunological condition and the outcome of lung adenocarcinoma (18). Furthermore, the expression of four PRG features strongly predicted a breast cancer patient's prognosis (19). Nonetheless, the predictive merit of pyroptosis gene signatures in the prostate has not been completely clarified.
Long non-coding RNA (lncRNA) does not have the proteincoding capacity, and its length exceeds 200 nucleotides (20). LncRNA has been shown to play key functions in a variety of biological and disease processes, including cancer (21,22). Growing evidence supports the involvement of lncRNA in PCa progression, including cell proliferation, apoptosis, metastasis, and invasion (23, 24). For example, overexpression of lncRNA PCAT14 inhibits the invasion of PCa cells and correlates with a good prognosis of PCa, which can be a diagnostic marker (25,26). However, the role of pyroptosisrelated lncRNAs (PRLs) in PCa still requires further exploration. Thus, exploring lncRNA biomarkers associated with pyroptosis in PCa is of clinical importance.
Machine learning is a branch of artificial intelligence that has been rapidly developed and applied in the field of medicine (27). Predictive models of diseases based on machine learning have been extensively mined (28). For example, Wu et al. used an ensemble of machine learning to develop a novel pyroptosis scoring system based on six lncRNAs to predict the prognosis of patients with low-grade glioma (29). In the present study, we used a machine learning approach to construct a PRL risk signature for PCa prognosis. Then, we investigated the associations of risk score models and clinical features, immune microenvironment, immune checkpoints, genetic mutations, and drug sensitivity to provide potential diagnostic and prognostic biomarkers for PCa. This study may help to understand the effect of PRL on PCa prognosis.

Data sets and pretreatments
The Cancer Genome Atlas (TCGA)-PRAD (Prostate Adenocarcinoma) dataset was downloaded from UCSC Xena (https://xenabrowser.net/). The data processing was performed to obtain FPKM data directly from TCGA and convert it into TPM value, normalized by log 2 (TPM+1). The GSE116918 (GPL25318) dataset was from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds/). For this validation cohort, 248 localized/locally advanced PCa patients commencing radical radiotherapy (with androgen deprivation therapy (ADT)) were included. The Affymetrix platform was utilized to generate raw data from the GSE116918 (GPL25318) dataset. The robust multi-chip averaging (RMA) algorithm was used to achieve background correction and normalization.

Pathway and immune-infiltration evaluation
The single-sample gene set enrichment analysis (ssGSEA) algorithm was used to quantify the abundance of 28 immune  (38) in PRAD and to compare immune infiltration with prognostic scores. Stromal score, Immune Score, ESTIMATE Score, and Tumor Purity were assessed with the ESTIMATE package, and the relationship between these scores and prognostic scores was compared. The GSVA package was used for GSVA analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The correlation analysis was performed with prognostic and functional enrichment pathway scores. Correlation analysis was performed with prognostic scores and all genes, and then gene set enrichment analysis (GSEA) was performed by the clusterProfiler package.

Gene mutation and copy number variation analysis
The gene mutations of the two groups were compared, and the gene mutation patterns were checked. The somatic mutations and somatic copy number variation (CNV) profiles were gathered from the TCGA-PRAD datasets. The Genomic Identification of Significant Targets in Cancer (GISTIC) analysis was performed to evaluate the genomic features. The CNV landscape based on ITGA5 levels and the copy number gains or losses at the amplified or deleted peaks were assessed by GISTIC 2.0 analysis (https://gatk.broadinstitute.org).

Drug prediction
Information on the sensitivity of tumor cell lines to potential drugs was downloaded from Cancer Therapeutics Response Portal 2 (CTRP v2) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM). The lower the area under the curve (AUC) of the cell line, the higher the sensitivity to the potential drug. Expression of cancer cell lines was downloaded from Cancer Cell Line Encyclopedia (CCLE). Predictions were made using the R package of pRRophetic.
Total RNA was obtained using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) from RWPE1, PC-3, and DU145 cells. RNA was reverse transcribed into cDNA using an mRNA reverse transcription kit (CW2569, CWBIO, Beijing, China). LncRNA expression was detected using the SYBR method (CW2601, CWBIO, China) and quantitative reverse transcription PCR (RT-qPCR) analysis with GAPDH as an internal reference. The primer sequences are shown in Table 2. The relative expression levels of genes were investigated by 2 −DDCt .

5-Ethynyl-2′-deoxyuridine assay
The 5-ethynyl-2′-deoxyuridine (EDU) assay kit (Guangzhou RiboBio, Guangzhou, China) was used to monitor cell proliferation. The cells were inoculated into 96-well plates (1 × 10 4 /well) until 80% confluence. Each well was added with 100 ml of EDU solution and incubated for 2 h. The cells were incubated with 4% paraformaldehyde at room temperature for 30 min. Then, the cells were treated with 100 ml of 1× Apollo ® staining reaction solution for 30 min. Next, 100 ml of Hoechst 33342 reaction solution was added to each well and incubated for 30 min. A microscope (DSZ2000X, Beijing Cnmicro Instrument Co., Ltd., Beijing, China) was used to observe and take pictures.

Wound healing assay
Cells (1 × 10 5 /well) were plated in 6-well plates until they achieved about 90% confluence. A 1-ml pipette tip was used to create scratch wounds, and photographs of the wounds (time 0 h) were immediately taken. Then, the cells were cultured in a serum-free medium. After incubation with 5% CO 2 at 37°C for 48 h, photographs were taken again.

Transwell assay
The invasion ability of cells was evaluated using the transwell assay. Transwell chambers (3428, Corning, New York, NY, USA) were pre-cooled overnight at 4°C one day in advance. Then, 100 ml of Matrigel dilute in serum-free medium was added to each well. The transfected cells were suspended in a serum-free medium and added to the transwell chamber; 500 ml of 10% fetal bovine serum complete medium was placed into the lower chamber. The cells were incubated at 37°C for 48 h. The upper chamber was removed and washed three times with phosphatebuffered saline (PBS), and the cells in the upper layer of the membrane were wiped off with a cotton ball. Cells were fixed with 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet solution for 5 min. Cells were observed under an inverted microscope, and three fields of view were taken. The number of invasive cells was recorded.

Statistical analysis
The data were mainly visualized using the R package ggplot2. The Shapiro-Wilk normality test was used to evaluate for normality of variables. For normally distributed variables, significant quantitative differences were determined by twotailed t-tests or one-way ANOVA. For non-normally distributed variables, significant quantitative differences were determined by the Wilcoxon test or the Kruskal-Wallis test. The Benjamini-Hochberg method was used, which converts pvalues to false discovery rate (FDR) to identify significant genes. The log-rank test was used to determine the statistical differences in each dataset. The Survminer R package was used to generate survival curves. Receiver operating characteristic (ROC) curves were drawn using the pROC package. All heatmaps were generated based on pheatmap. All statistical analyses were performed in R (https://www.r-project.org/). p < 0.05 was considered statistically significant.

Construction of pyroptosis-related lncRNA signatures
Forty-five pyroptosis genes were obtained from the literature, and the pyroptosis score was calculated by the GSVA method. The correlation between lncRNAs and pyroptosis scores was analyzed by Spearman's correlation test. The lncRNAs with |correlation coefficient| > 0.3 and p < 0.05 were selected, and 553 lncRNAs were obtained. These lncRNAs were subjected to univariate analysis, and 27 lncRNAs (p < 0.05) were screened ( Figure 1A). The single-factor meaningful genes were selected for survival random forest analysis, and six important lncRNAs were screened ( Figure 1B, Figure S2A). These important gene variables were then used for Lasso analysis to build a risk scoring model ( Figure 1C). The risk score was 0.4242 × AC005253.1 + 0.8249 × LIMD1-AS1 − 1.6456 × LINC01852 + 0.3408 × AC127502.2 + 1.5518 × AC068580.3 − 1.7451 × AC129507.1. With the median risk score as the cutoff value, PCa patients were categorized into high-score and low-score groups. PCa patients in the high-score group had poorer overall survival than those in the low-score group ( Figure 1D). Furthermore, the AUC values of the 1-, 3-, and 5-year ROC curves were 1, 0.93, and 0.92, respectively ( Figure 1E). The clinical characteristic score showed that the risk score of patients aged ≥45 was higher than that of patients aged <45, but there was no significant difference (p = 0.15). Patients with stage N1 had a higher risk score than patients with stage N0 (p = 0.00039). T4 stage patients had higher risk scores than T3 stage and T2 stage patients, and T3 stage patients had higher risk scores than T2 stage patients (p = 0.0014). Moreover, patients with the status alive had a lower risk score than patients with the status dead (p = 0.001) ( Figure 1F). A publicly available dataset (GSE116918) was used to validate the reliability of the constructed risk scoring model. Consistent with the findings from the TCGA-PRAD cohort, survival analysis showed that patients in the high-score group had lower survival rates than those in the low-score group ( Figure S2B). In addition, the AUC values of the 1-, 3-, and 5-year ROC curves were 0.7, 0.71, and 0.77, respectively ( Figure S2C). To investigate whether the constructed risk scoring model was independent of clinicopathological parameters, univariate and multivariate Cox regression analyses were performed on age, T stage, N stage, and risk score. Risk score was the parameter independently predicting overall survival ( Figures S2D, E). The predictive model could be considered an independent prognostic factor in PCa patients.

Correlation of risk scores with pyroptosis genes and immune infiltration
We surveyed the relationship between model genes and risk scores. Risk scores were favorably associated with LIMD1-AS1, AC127502.2, AC005253.1, and AC06850.3 and negatively associated with LINC01852 and AC129507.1 (Figures 2A, B). Next, we constructed a heatmap of risk scores and pyroptosis gene correlations. The results showed that ARRDC1-AS1, GPX4, GSDMD, GSDME, and NLRP3 were substantially associated with the risk score ( Figure 2C). We used the ESTIMATE package and ssGSEA algorithm to evaluate immune infiltration. Among them, Stromal score, Immune Score, and ESTIMATE Score were negatively associated with risk score, while Tumor Purity was positively associated with risk score ( Figure 2D). B cell, T cell, Macrophage, and Natural killer cell were significantly associated with risk score ( Figure 2D). For example, Activated B cell, Activated CD8 T cell, and Activated dendritic cell were negatively associated with risk scores. Activated CD4 T cell was positively associated with risk score.

Immune checkpoint
We determined the expression of seven classes of immune checkpoint molecules in low-and high-risk-scoring populations. As shown in the Antigen present classification, HLA-A, HLA-B, HLA-DPA1, HLA-DPB1, HLA-DQB2, HLA-DRB1, and MICA were expressed at a low level in the high-score group. In Cell adhesion, SELP was expressed at a high level in the low-score group. In Ligand, CCL5, CX3CL1, and TGFB1 were expressed at a high level in the low-score group. In Receptor, CD27, CD40, EDNRB, and TLR4 were expressed at a high level in the lowscore group. In Co-inhibitor, CD276 was expressed at a low level in the low-score group, and PDCD1LG2 and VTCN1 were expressed at a high level in the low-score group. In addition, HMGB1 was expressed at a low level in the low-score group, while ENTPD1 and PRF1 were expressed at a high level in the low-score group (Figure 3).

Functional analysis of risk score
We used the GSVA package for GO and KEGG enrichment analyses. Most samples were enriched for pathways closely related to tumorigenesis. Examples included DNA replication, cell cycle, and mTOR signaling pathway. These pathways were positively associated with risk scores ( Figure 4A). Correlation analysis with risk score and functional enrichment pathway score was performed. In addition to the immune checkpoint, risk scores were significantly associated with 16 other pathways ( Figure 4B). Risk scores were positively correlated with cell cycle, DNA replication, DNA damage repair, and WNT target while negatively correlated with CD8 T effector and antigen processing machinery. We conducted a relevant analysis with the risk score and all genes and then performed a GSEA with the clusterProfiler package ( Figure S3). The Hippo signaling pathway was downregulated.

Drug sensitivity analysis of risk scores in two groups
We downloaded information on the susceptibility of tumor cell lines to potential drugs from the CTRP v2 and PRISM. The lower the AUC of the cell line, the higher the sensitivity to the potential drug. These data revealed that among anticancer drugs, including ML258, 16-beta-bromoandrosterone, VU0155056, BRD-K02251932, BRD-K85133207, imiquimod, temoporfin, SGI-1027, and eptifibatide, the sensitivity of patients in the high-score group to the drugs was significantly increased than in the low-score group ( Figure 6).

Silencing of AC005253.1 promoted pyroptosis of prostate cancer cells
We further tested the effect of AC005253.1 on the pyroptosis of PC-3 and DU145 cells by Western blotting experiment. The results showed that after silencing AC005253.1, the expression of the inflammasomes (AIM2, NLRC4, and NLRP3) was altered ( Figure 9A). Compared with the si-NC group, the expression of Immune checkpoint. Immune checkpoint molecule expression in low-and high-score groups. *p < 0.05. **p < 0.01. ***p < 0.001 ****p < 0.0001. ns, not significant.

Discussion
PCa is one of the most common tumors in men. Due to its heterogeneity and progressive nature, it remains incurable (41). Valid prognostic models based on specific biomarkers can accurately predict survival outcomes for the effective management of PCa patients (42). Pyroptosis-related lncRNA risk prediction models have been reported to be expected to assist in the treatment and management of various tumors (43,44). Our study used a novel risk model of six PRLs (AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852) developed using Lasso analysis. AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852 were identified as PRLs for the first time. The results showed that the risk score model had the best ability to distinguish clinical characteristics between the high-risk and low-score groups significantly. In our proposed model, the AUC values of the ROC curves for 1-, 3-, and 5-year PCa were 1, 0.93, and 0.92, respectively. In addition, the 1-, 3-, and 5-year AUC values in the test set also had desirable results. Our risk model had excellent predictive power compared to other published pyroptosis-based prognostic models in PCa (45,46). Different from the direct use of Lasso to build a prognostic model of eight pyroptosis-related genes in the study of A B FIGURE 4 Functional analysis of risk score. (A) Heatmap for GO and KEGG analyses using GSVA package. (B) Correlation of risk score with functional enrichment pathways. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSVA, gene set variation analysis. *p < 0.05. **p < 0.01. ***p < 0.001 ****p < 0.0001. A B FIGURE 5 CNV and mutation analysis in high-and low-score groups. (A) CNV maps for groups with high and low score. (B) Top 30 gene mutation frequencies in two groups. CNV, copy number variation. *p < 0.05 Wang et al. (47), we used random forest dimensionality reduction and screening methods and further used Lasso analysis to build a predictive model. This analytical approach may help improve the predictive accuracy of the risk model signature.
It has been reported that pyroptosis is closely related to the tumor immune microenvironment. The release of inflammatory factors is caused by pyroptosis triggers powerful antitumor immunity (48). The infiltration of CD8+ T cells and natural killer cells in the pyroptosis-activated immune microenvironment can promote pyroptosis and form a positive feedback loop (49). Previous studies have shown that increased CD8+ T-cell infiltration is independently associated with improved survival after radical prostatectomy (50). Men with more CD4+ T cells in the prostate tumor environment have an increased risk of dying from PCa (51). B-cell activation is thought to be a driver of the PCa immune response and improves postoperative survival (52). Overall, the poor prognosis and outcome of PCa are closely related to pyroptosis-triggered immune cell infiltration, which is consistent with our results. Our results showed that the risk score was negatively correlated with activated B cells, CD8 T cells, and dendritic cells while positively correlated with activated CD4 T cells. Infiltration levels of B cells, CD8 T cells, and dendritic cells decreased with increasing risk scores, consistent with shorter survival times in patients with high scores. Drug sensitivity analysis of risk scores in two groups. Information on the sensitivity of tumor cell lines to potential drugs was downloaded from CTRP v2 and PRISM. Prediction of drug susceptibility in two groups using pRRophetic. The lower the AUC of the cell line, the higher the sensitivity to the potential drug. AUC, area under the curve. ***p < 0.001 Risk score gene expression identification. RT-qPCR to detect the AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852 expression in RWPE1, PC-3, and DU145 cell. *p < 0.05, vs. RWPE1.
We compared the expression of immune checkpoints in the high-risk and low-risk groups and found that some immune checkpoints such as HLA and MICA were expressed at a high level in the low-risk group. PCa downregulated the expression of the HLA-1 antigen processing machinery (APM) and had defects in the antigen presentation pathway (53). Low expression of MICA is associated with poorer overall survival in PCa and is associated with aggressiveness (54). This suggests that our signature could effectively identify the status of immune checkpoints in different PCa patients, providing new ideas for their treatment.
CNVs were regions of the genome with integer copy number changes, including amplifications and deletions of DNA sequences, that could drive cancer's rapid adaptive evolution and progression (55). The CNV results uncovered significant differences in mutation status between the high-score and lowscore groups. In the high-score group, the gene with the highest mutation frequency was TP53 (17%), while in the low-risk group, it was SPOP (11%). TP53 mutation was the most common genetic alteration that played a major role in the pathogenesis of PCa (56,57). SPOP mutations were associated with improved overall survival, whereas TP53 mutations were associated with poorer survival in secondary metastatic hormone-sensitive PCa (58). These data implicated that the high-score group might have more tumorigenic gene mutations.
To better assess the risk model's clinical feasibility, we analyzed information on the sensitivity of tumor cell lines to potential drugs. The results showed that the high-risk group cell lines were significantly less sensitive to drugs such as importazole and imiquimod. Importazole, a specific inhibitor that alters the interaction of KPNB1 with RanGTP, has a good inhibitory effect on PCa progression (59). Imiquimod (also known as a TLR7 agonist) inhibits the growth of mouse (TRAMP C2) and human PCa cells and can be used as an alternative therapy for locally generated PCa (60). Our findings suggest that the low-risk group is more likely to benefit from these drugs. Collectively, these findings may provide prospective treatment options for PCa patients.
Notably, RT-qPCR analysis confirmed high expression of AC129507.1, AC005253.1, AC127502. Frontiers in Oncology frontiersin.org AC068580.3, and LIMD1-AS1 were thought to play important roles in different cancers, while AC005253.1, AC127502.2, and LINC01852 were identified for the first time. AC129507.1 was identified as an on-risk gene in risk models for prognosis patients with gastric adenocarcinoma (61). AC068580.3 was identified as an autophagy-related lncRNA as an indication of prognosis for colon adenocarcinoma (62). LIMD1-AS1 inhibited lung cancer progression by inhibiting cell multiplication and promoting apoptosis (63). Our study demonstrated that inhibiting the expression of AC005253.1 could inhibit cell viability, migration, and invasion. We further detected the expression of the inflammasome (AIM2, NLRC4, and NLRP3) and found that silencing of AC005253.1 could significantly increase the expression of the AIM2 inflammasome. Activation of the AIM2 inflammasome can promote pyroptosis (64). We also found that inhibition of AC005253.1 could promote pyroptosis in PCa cells. Therefore, we speculate that AC005253.1 may affect pyroptosis through the AIM2 inflammasome in PCa. In the present study, we report for the first time the relationship between AC005253.1 and pyroptosis in PCa.
In conclusion, we successfully established an efficient forecast PCa model based on six PRLs, including AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852. This well-validated model built on these six PRLs will provide new insights into identifying PCa prognosis. Through in vitro experiments, we verified that silencing of AC005253.1 could inhibit the proliferation, migration, and invasion of PCa cells. In addition, silencing of AC005253.1 might promote pyroptosis by affecting the expression of AIM2 in PCa.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Author contributions
JL conceived the project. RT performed the experiments. JY analyzed the data. JY and JL wrote the paper. All authors contributed to the article and approved the submitted version.